GNN architecture.
We mainly experiment on Graph Isomorphism Networks (GINs) [1]},
the most expressive GNN architecture for graph-level prediction tasks.
We also experiment with other popular architectures: GCN [2]}, GraphSAGE
 [3]} and GAT [4]}.
We select the same GNN hyper-parameters as previous works [5]}, [6]}.
For the proposed graph matching methods,
we adopt
dot production for the \(\operatorname{sim}(\cdot )\)  function
and select \(\tau =0.07\)  in Eqn. (REF ). We utilize a multiple layer perceptron (MLP) as function of \(\operatorname{Update}\)  in Eqn. (REF ).
